
Inadequate modeling of interaction mechanism.
Existing works [1]}, [2]}, [3]} learn molecular representation and make prediction based on whole molecular structure of drugs and proteins, ignoring that the interactions are sub-structural– only involving relevant sub-structures of drugs and proteins [4]}, [5]}. The full-structural molecular representations introduce noises and affect the prediction performance. Also, the learned representations are hard to interpret since they do not provide a tractable path to indicate which sub-structures of drugs and proteins contribute to the interactions.

Restricted to limited labeled data.
Previous works [1]}, [2]}, [3]}, [9]}, [10]} focus on data in hand and limit the scope within several thousands of drugs and proteins while ignoring the vast (e.g., order of millions) unlabelled biomedical data available. The model architectures in previous works also are not designed to enable the integration of massive dataset.

